In this senior role, you will lead the design, development, testing, and
deployment of highly scalable, high-performance data integration and
transformation solutions across Quest’s enterprise data platform and the HAS
data product. Partnering with architects, business customers, and
cross-functional engineering and data science teams, you will shape data
architecture strategy, design large-scale data processing solutions, and build
the data-driven systems that guide Quest’s reporting and analytics. This
includes defining standards, reusable patterns, and best practices for mining,
acquiring, transforming, standardizing, enhancing, migrating, verifying, and
modeling Quest’s enterprise data. In addition, you will drive advanced analytics
initiatives that build, train, deploy, and refine AI/ML models to efficiently
analyze vast quantities of data, and you will provide technical leadership and
mentorship to fellow engineers to best serve our patient population.
RESPONSIBILITIES
Serve as a senior technical advisor and subject matter expert to business
customers, architects, and internal teams, solving the most complex data
challenges related to healthcare analytics.
Lead the design and architecture of end-to-end data solutions, translating
business requirements into scalable, reusable, and well-documented technical
designs.
Define, drive, and govern data architecture standards, design patterns, and
engineering best practices across the data engineering organization.
Engineer and oversee the preparation of internal and customer-facing
datasets, ensuring strict adherence to defined technical specifications,
internal data standards, and external Statements of Work (SOWs).
Architect, develop, and optimize robust, scalable data pipelines to acquire,
transform, and provision data for analytics and data science initiatives.
Design and build performant, scalable data models and warehouse structures
within cloud data warehouses (e.g., Google BigQuery, Snowflake) and guide
their long-term evolution.
Partner with SD3 Data Scientists to productionize, operationalize, and manage
the handoff of machine learning model inferences into our persistent data
stores.
Establish and champion modern DevSecOps standards, including CI/CD, automated
testing, and version control using GitHub, across the team.
Ensure all data solutions comply with data governance, security policies, and
healthcare regulations (e.g., HIPAA), and help define and improve those
policies.
Provide technical leadership and mentorship to junior and mid-level
engineers, including leading design reviews and code reviews.
Lead organizational improvements in processes and technology by evaluating,
recommending, and adopting new tools and best practices in data engineering.
Define and lead unit, integration, and performance testing strategies to
ensure the quality, reliability, and scalability of data pipelines.
QUALIFICATIONS
Required Work Experience:
5-8 years of data development experience with a focus on designing and
building data pipelines and ETL processes
5-8 years of experience with the cloud (AWS, Azure and/or Google Cloud
Platform) – GCP experience highly preferred
5-8 years of experience in cloud-based data warehouses (Snowflake, Google
BigQuery, Amazon Redshift, Azure Synapse Analytics)
5-8 years of experience with cloud-based ETL/ELT tools (Matillion, Glue, Data
Factory). Matillion experience is strongly preferred.
2+ years in a technical lead or data/solution architecture capacity, leading
the design of large-scale data solutions
Bachelor’s Degree (Computer Science, Engineering, Information Systems,
Mathematics, Business Analytics, or relevant degree)
Preferred Work Experience:
Experience with version control systems (Git) and leading DevSecOps / CI-CD
practices
Understanding of and willingness to embrace Agile Principles (Scrum),
including serving as a technical lead
Experience mentoring engineers and conducting design and code reviews
Experience defining data architecture standards and data governance across
teams
Physical and Mental Requirements:
Open mindset, ability to quickly adapt new technologies and learn new
practice
Master’s Degree (Computer Science, Engineering, Information Systems,
Mathematics, Business Analytics, or relevant degree)
Knowledge:
Demonstrated advanced knowledge of SQL, Java, Python, C/C++, Scala, Julia,
and/or other modern data and analytics programming languages
Demonstrated expertise in data modeling principles, data architecture, and
database systems.
Familiarity with containerization technologies (e.g., Docker, Kubernetes) is
a plus.
Knowledge of data architecture frameworks, distributed systems, and
performance optimization at scale
Skills:
Data solution design and architecture
Coding
Data Warehousing
Database schema optimization
Database Systems
Technical leadership and mentoring
Critical thinking skills
Business communication
ETL
Quest Diagnostics honors our service members and encourages veterans to apply.
While we appreciate and value our staffing partners, we do not accept
unsolicited resumes from agencies. Quest will not be responsible for paying
agency fees for any individual as to whom an agency has sent an unsolicited
resume.
Equal Opportunity Employer: Race/Color/Sex/Sexual Orientation/Gender
Identity/Religion/National Origin/Disability/Vets or any other legally protected
status.